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Search indexed bioRxiv preprints in genomics, neuroscience, cell biology and bioinformatics. Read source abstracts and check manuscript versions; preprints are not peer reviewed.

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bioRxiv2021

Spatial and temporal autocorrelation weave human brain networks

High-throughput experimental methods in neuroscience have led to an explosion of techniques for measuring complex interactions and multi-dimensional patterns. However, whether sophisticated measures of emergent phenomena can be traced back to simpler low-dimensional statistics is largely unknown. To explore this question, we examine resting state fMRI (rs-fMRI) data using complex topology measures from network neuroscience. We show that spatial and temporal autocorrelation are reliable statistics which explain numerous measures of network topology. Surrogate timeseries with subject-matched spatial and temporal autocorrelation capture nearly all reliable individual and regional variation in these topology measures. Network topology changes during aging are driven by spatial autocorrelation, and multiple serotonergic drugs causally induce the same topographic change in temporal autocorrelation. This reductionistic interpretation of widely-used complexity measures may help link them to neurobiology.

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bioRxiv2021

Adaptable and Automated Rodent Behavior Maze System

Mazes are a fundamental and widespread tool in behavior and systems neuroscience research in rodents, especially in spatial navigation and spatial memory investigations in freely behaving animals. However, their form and inflexibility often restrict potential experimental paradigms that involve multiple or adaptive maze designs. Unique layouts often lead to elevated costs, whether financially or in terms of time investment from scientists. To alleviate these issues, we have developed an automated, modular maze system that is flexible and scalable. This open source Adapt-A-Maze (AAM) system will allow for experiments with multiple track configurations in rapid succession. Additionally, the flexibility can expedite prototyping of behavioral paradigms. Automation ensures less variability in experimental parameters and higher throughput. Finally, the standardized componentry enhances experimental repeatability within labs and replicability across labs. Our maze was successfully used across labs, in multiple experimental designs, with and without extracellular or optical recordings, in rats. The AAM system presents multiple advantages over current maze options and can facilitate novel behavior and systems neuroscience research. Significance statementWe have developed an open source, modular maze system (the Adapt-A-Maze, AAM) that enables any lab interested in rodent behavior and cognition to create standard and unique mazes for their research. The AAM uses modular track pieces that can be combined to create a wide array of maze types. The AAM system also included reward wells with lick detection and pneumatic barriers. All aspects of the maze can be controlled via TTL signals to automate tasks and improve repeatability of experiments. The AAM system is expected to help labs quickly and inexpensively set up recordable experiments to advance our understanding of the neurophysiology underlying behavior and cognition.

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bioRxiv2021

Open multimodal iEEG-fMRI dataset from naturalistic stimulation with a short audiovisual film

Intracranial human recordings are a valuable and rare resource that the whole neuroscience community can benefit from. Making such data available to the neuroscience community not only helps tackle the reproducibility issues in science, it also helps make more use of this valuable data. The latter is especially true for data collected using naturalistic tasks. Here, we describe a dataset collected from a large group of human subjects while they watched a short audiovisual film. The dataset is characterized by several unique features. First, it combines a large amount of intracranial data from 51 intracranial electroencephalography (iEEG) participants, who all did the same task. Second, the intracranial data are accompanied by fMRI recordings acquired for the same task in 30 functional magnetic resonance imaging (fMRI) participants. Third, the data were acquired using a rich audiovisual stimulus, for which we provide detailed speech and video annotations. This multimodal dataset can be used to address questions about neural mechanisms of multimodal perception and language comprehension as well as the nature of the neural signal acquired during the same task across brain recording modalities.

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bioRxiv2021

The Consciousness Theories Studies (ConTraSt) database: analyzing and comparing empirical studies of consciousness theories

Understanding how consciousness arises from neural activity remains one of the biggest challenges for neuroscience. Numerous theories have been proposed in recent years, each gaining independent empirical support. Currently, there is no comprehensive, quantitative and theory-neutral overview of the field that enables an evaluation of how theoretical frameworks interact with empirical research. We provide a birds eye view on studies that interpreted their findings in light of at least one of four leading neuroscientific theories of consciousness (N=412 experiments), asking how methodological choices of the researchers might affect the final conclusions. We found that supporting a specific theory can be predicted solely from methodological choices, irrespective of findings. Furthermore, most studies interpret their findings post-hoc, rather than a-priori testing critical predictions of the theories. Our results highlight challenges for the field and provide researchers with a unique, open-access website to further analyze trends in the neuroscience of consciousness.

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bioRxiv2021

A Framework for Brain Atlases: Lessons from seizure Dynamics

Brain maps, or atlases, are essential tools for studying brain function and organization. The abundance of available atlases used across the neuroscience literature, however, creates an implicit challenge that may alter the hypotheses and predictions we make about neurological function and pathophysiology. Here, we demonstrate how parcellation scale, shape, anatomical coverage, and other atlas features may impact our prediction of the brains function from its underlying structure. We show how network topology, structure-function correlation (SFC), and the power to test specific hypotheses about epilepsy pathophysiology may change as a result of atlas choice and atlas features. Through the lens of our disease system, we propose a general framework and algorithm for atlas selection. This framework aims to maximize the descriptive, explanatory, and predictive validity of an atlas. Broadly, our framework strives to provide empirical guidance to neuroscience research utilizing the various atlases published over the last century.

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bioRxiv2021

Whole-brain studies of spontaneous behavior in head-fixed rats enabled by zero echo time MB-SWIFT fMRI

Understanding the link between the brain activity and behavior is a key challenge in modern neuroscience. Behavioral neuroscience, however, lacks tools to record whole-brain activity in complex behavioral settings. Here we demonstrate that a novel Multi-Band SWeep Imaging with Fourier Transformation (MB-SWIFT) functional magnetic resonance imaging (fMRI) approach enables whole-brain studies in spontaneously behaving head-fixed rats. First, we show anatomically relevant functional parcellation. Second, we show sensory, motor, exploration, and stress-related brain activity in relevant networks during corresponding spontaneous behavior. Third, we show odor-induced activation of olfactory system with high correlation between the fMRI and behavioral responses. We conclude that the applied methodology enables novel behavioral study designs in rodents focusing on tasks, cognition, emotions, physical exercise, and social interaction. Importantly, novel zero echo time and large bandwidth approaches, such as MB-SWIFT, can be applied for human behavioral studies, allowing more freedom as body movement is dramatically less restricting factor.

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bioRxiv2021

Effects of tDCS on the attentional blink revisited: A statistical evaluation of a replication attempt

The attentional blink (AB) phenomenon reveals a bottleneck of human information processing: the second of two targets is often missed when they are presented in rapid succession among distractors. In our previous work, we showed that the size of the AB can be changed by applying transcranial direct current stimulation (tDCS) over the left dorsolateral prefrontal cortex (lDLPFC) (London & Slagter, Journal of Cognitive Neuroscience, 33, 756-68, 2021). Although AB size at the group level remained unchanged, the effects of anodal and cathodal tDCS were negatively correlated: if a given individuals AB size decreased from baseline during anodal tDCS, their AB size would increase during cathodal tDCS, and vice versa. Here, we attempted to replicate this finding. We found no group effects of tDCS, as in the original study, but we no longer found a significant negative correlation. We present a series of statistical measures of replication success, all of which confirm that both studies are not in agreement. First, the correlation here is significantly smaller than a conservative estimate of the original correlation. Second, the difference between the correlations is greater than expected due to sampling error, and our data are more consistent with a zero-effect than with the original estimate. Finally, the overall effect when combining both studies is small and not significant. Our findings thus indicate that the effects of lDPLFC-tDCS on the AB are less substantial than observed in our initial study. Although this should be quite a common scenario, null findings can be difficult to interpret and are still under-represented in the brain stimulation and cognitive neuroscience literatures. An important auxiliary goal of this paper is therefore to provide a tutorial for other researchers, to maximize the evidential value from null findings.

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bioRxiv2021

Harnessing the Power of Sex Differences: What a Difference Ten Years Did Not Make

Sex differences exist in many neurological and psychiatric diseases. Mandates have been initiated across funding agencies for research to include males and females. What has been lacking in the literature is a detailed assessment of how sex is incorporated into the design (e.g. balanced design) and into the analyses (e.g. covariate). We surveyed papers in 2009 and 2019 across six journals in Neuroscience and Psychiatry. There was a 30% increase in the percentage of papers that included both sexes to 68% in 2019. Despite this increase, in 2019 only 19% of studies used an optimal design for discovery of possible sex differences and only 5% analyzed sex as a discovery variable. Here we show that little progress has been made in harnessing the power that sex differences can afford in research for discovery and therapeutic potential for neurological and psychiatric disease to improve the health of men, women and gender diverse individuals. Highlights68% of Neuroscience and Psychiatry papers reported the use of both sexes in 2019 Only 19% of studies in 2019 used sex consistently throughout the study analyses Of the studies that used males and females, 59% did not include sex in the analyses Only 5% of studies in 2019 used sex as a discovery variable in their analyses Male only papers were 8.4 times more prevalent than female-only papers

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bioRxiv2021

Luminance and contrast of images in the THINGS database

The THINGS database is a freely available stimulus set that has the potential to facilitate the generation of theory that bridges multiple areas within cognitive neuroscience. The database consists of 26,107 high quality digital photos that are sorted into 1,854 concepts. While a valuable resource, relatively few technical details relevant to the design of studies in cognitive neuroscience have been described. We present an analysis of two key low-level properties of THINGS images, luminance and luminance contrast. These image statistics are known to influence common physiological and neural correlates of perceptual and cognitive processes. In general, we found that the distributions of luminance and contrast are in close agreement with the statistics of natural images reported previously. However, we found that image concepts are separable in their luminance and contrast: we show that luminance and contrast alone are sufficient to classify images into their concepts with above chance accuracy. We describe how these factors may confound studies using the THINGS images, and suggest simple controls that can be implemented a priori or post-hoc. We discuss the importance of using such natural images as stimuli in psychological research.

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bioRxiv2021

Minimizing the ex vivo confounds of cell-isolation techniques on transcriptomic profiles of purified microglia

Modern molecular neuroscience studies require analysis of specific cellular populations derived from brain tissue samples to disambiguate cell-type specific events. This is particularly true in the analysis of minority glial populations in the brain, such as microglia, which may be obscured in whole tissue analyses. Microglia have central functions in development, aging, and neurodegeneration and are a current focus of neuroscience research. A long-standing concern for glial biologists using in vivo models is whether cell isolation from CNS tissue could introduce ex vivo artifacts in microglia, which respond quickly to changes in the environment. Mouse microglia were purified by magnetic-activated cell sorting (MACS), as well as cytometer- and cartridge-based fluorescence-activated cell sorting (FACS) approaches to compare and contrast performance. The Cx3cr1-NuTRAP mouse model was used here to provide an endogenous fluorescent microglial marker and a microglial-specific translatome profile as a baseline comparison lacking cell isolation artifacts. All methods performed similarly for microglial purity with main differences being in cell yield and time of isolation. Ex vivo activation signatures occurred principally during the initial tissue dissociation and cell preparation and not the microglial cell sorting. Utilizing transcriptional and translational inhibitors during the cell preparation prevented the activational phenotype. These data demonstrate that a variety of microglial isolation approaches can be used, depending on experimental needs, and that inhibitor cocktails are effective at reducing cell preparation artifacts. Table of Contents Image O_FIG O_LINKSMALLFIG WIDTH=200 HEIGHT=97 SRC="FIGDIR/small/452509v1_ufig1.gif" ALT="Figure 1"> View larger version (19K): org.highwire.dtl.DTLVardef@823562org.highwire.dtl.DTLVardef@7edb25org.highwire.dtl.DTLVardef@656feeorg.highwire.dtl.DTLVardef@197aae1_HPS_FORMAT_FIGEXP M_FIG C_FIG Main PointsMACS, cytometer-FACS, and cartridge-FACS give equivalent and sufficient yield/purity for microglial analyses. Ex vivo microglial activation is prevented by supplementation with transcription/translation inhibitors during cell preparation.

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bioRxiv2021

Neural Collision Detection: an open source library to study the three-dimensional interactions of neurons and other tree-like structures

AO_SCPLOWBSTRACTC_SCPLOWThe analysis of neuronal structure and its relation to function has become a fundamental pillar in neuroscience since its earliest days, with the underlying premise that morphological properties can modulate neuronal computations. It is often the case that the rich three-dimensional structure of neurons is quantified by tools developed in other fields, such as graph theory and computational geometry; nevertheless, some of the more advanced tools developed in these fields have not yet been made accessible to the neuroscience community. Here we present Neural Collision Detection, a library providing high-level interfaces to collision-detection routines and alpha shape calculations, as well as statistical analysis and visualizations for 3D objects, with the aim to lower the entry gap for neuroscientists into these worlds. Our work here also demonstrates a variety of use cases for the library and exemplary analysis and visualizations that were carried out with it on real neuronal and vascular data.

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bioRxiv2021

The Normative Modeling Framework for Computational Psychiatry

Normative modeling is an emerging and innovative framework for mapping individual differences at the level of a single subject or observation in relation to a reference model. It involves charting centiles of variation across a population in terms of mappings between biology and behavior which can then be used to make statistical inferences at the level of the individual. The fields of computational psychiatry and clinical neuroscience have been slow to transition away from patient versus "healthy" control analytic approaches, likely due to a lack of tools designed to properly model biological heterogeneity of mental disorders. Normative modeling provides a solution to address this issue and moves analysis away from case-control comparisons that rely on potentially noisy clinical labels. In this article, we define a standardized protocol to guide users through, from start to finish, normative modeling analysis using the Predictive Clinical Neuroscience toolkit (PCNtoolkit). We describe the input data selection process, provide intuition behind the various modeling choices, and conclude by demonstrating several examples of down-stream analyses the normative model results may facilitate, such as stratification of high-risk individuals, subtyping, and behavioral predictive modeling. The protocol takes approximately 1-3 hours to complete.

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bioRxiv2021

Modeling Neural Variability in Deep Networks with Dropout

Computational neuroscience studies have shown that the structure of neural variability to an unchanged stimulus affects the amount of information encoded. Some artificial deep neural networks, e.g. those with Monte Carlo dropout layers, also have variable responses when the input is fixed. However, the structure of the trial-by-trial neural co-variance in neural networks with dropout has not been studied and its role in decoding accuracy is unknown. We studied the above questions in a convolutional neural network model with dropout in both the training and testing phase. We found that trial-by-trial correlation between neurons, i.e. noise correlation, is positive and low-dimensional. Neurons that are close in a feature map have larger noise correlation. These properties are surprisingly similar to the findings in the visual cortex. We further analyzed the alignment of the main axes of the covariance matrix. We found that different images share a common trial-by-trial noise covariance subspace, and they are aligned with the global signal covariance. The above evidence that the noise covariance is aligned with signal covariance suggests that noise covariance in dropout neural networks reduces network accuracy, which we further verified directly with a trial-shuffling procedure commonly used in neuroscience. These findings highlight a previously overlooked as-pect of dropout layers that can affect network performance. Such dropout networks could also potentially be a computational model of neural variability.

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bioRxiv2021

Patch-Clamp Proteomics of Single Neuronal Somas in Tissue Using Electrophysiology and Subcellular Capillary Electrophoresis Mass Spectrometry

Understanding of the relationship between cellular function and molecular composition holds a key to next-generational therapeutics but requires measurement of all types of molecules in cells. Developments in sequencing enabled semi-routine measurement of single-cell genomes and transcriptomes, but analytical tools are scarce for detecting diverse proteins in tissue-embedded cells. To bridge this gap for neuroscience research, we report the integration of patch-clamp electrophysiology with subcellular shot-gun proteomics by high-resolution mass spectrometry (HRMS). Recording of electrical activity permitted identification of dopaminergic neurons in the substantia nigra pars compacta. Ca. 20-50% of the neuronal soma content, containing an estimated 100 pg of total protein, was aspirated into the patch pipette filled with ammonium bicarbonate. About ~1 pg of somal protein, or ~0.25% of the total cellular proteome, was analyzed on a custom-built capillary electrophoresis (CE) high-resolution mass spectrometer (HRMS). A series of experiments were conducted to systematically enhance detection sensitivity through refinements in sample processing and detection, allowing us to quantify ~275 different proteins from somal aspirate-equivalent protein digests. From single neurons, patch-clamp proteomics of the soma quantified 91, 80, and 95 different proteins from 3 different dopaminergic neurons, or 157 proteins in total. Quantification revealed detectable proteomic differences between the somal protein samples. Analysis of canonical knowledge predicted rich interaction networks between the proteins. The integration of patch-clamp electrophysiology with subcellular CE-HRMS proteomics expands the analytical toolbox of neuroscience.

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bioRxiv2021

A Compressive Sensing Approach to Inferring Cognitive Representations with Reverse Correlation

Uncovering high-level cognitive representations of categories such as faces is an elusive goal that has been frequently sought using reverse correlation, a technique employed in fields ranging from neurophysiology to cognitive psychology. In reverse correlation, subjects are asked to make perceptual judgments (e.g., "do you see a face?") about richly varying stimuli (e.g., white noise), and observed responses are then regressed against stimuli to yield reconstructions of the underlying cognitive representation. However, many thousands of stimulus-response pairs are frequently required, which severely limits the breadth of studies that are feasible using this powerful method. Techniques that are currently employed to improve efficiency, such as filtering the reconstruction, nevertheless bias the outcome. Here, we show that an advanced signal processing technique for improving sampling efficiency - compressive sensing - is directly compatible with reverse correlation. A trio of simulations are performed to demonstrate that compressive sensing can reduce the required stimulus-response pairs by up to 90% without biasing the reconstruction or can retrospectively improve the accuracy of the reconstructions on existing data. This work concludes by outlining the potential of compressive sensing to improve representation reconstruction throughout the field of neuroscience and beyond. Significance StatementUncovering cognitive representations is an elusive goal that is increasingly pursued using the reverse correlation method, wherein human subjects make judgments about vague stimuli. Employing reverse correlation often entails collecting thousands of stimulus-response pairs, which severely limits the breadth of studies that are feasible using the method. Here we show that this methodological barrier can be overcome using compressive sensing, an advanced signal processing technique designed to improve sampling efficiency. Three sets of simulations are performed to demonstrate that compressive sensing can improve the accuracy of reconstructed cognitive representations and dramatically reduce the required number of stimulus-response pairs. This work concludes by outlining the potential of compressive sensing to improve representation reconstruction throughout the field of neuroscience and beyond.

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bioRxiv2021

Metabolic and functional connectivity provide unique and complementary insights into cognition-connectome relationships

A major challenge in current cognitive neuroscience is how functional brain connectivity gives rise to human cognition. Functional magnetic resonance imaging (fMRI) describes brain connectivity based on cerebral oxygenation dynamics (hemodynamic connectivity), whereas [18 F]-fluorodeoxyglucose functional positron emission tomography (FDG-fPET) describes brain connectivity based on cerebral glucose uptake (metabolic connectivity), each providing a unique characterisation of the human brain. How these two modalities differ in their contribution to cognition and behaviour is unclear. We used simultaneous resting-state FDG-fPET/fMRI to investigate how hemodynamic connectivity and metabolic connectivity relate to cognitive function by applying partial least squares analyses. Results revealed that while for both modalities the frontoparietal anatomical subdivisions related the strongest to cognition, using hemodynamic measures this network expressed executive functioning, episodic memory, and depression, while for metabolic measures this network exclusively expressed executive functioning. These findings demonstrate the unique advantages that simultaneous FDG-PET/fMRI has to provide a comprehensive understanding of the neural mechanisms that underpin cognition and highlights the importance of multimodality imaging in cognitive neuroscience research.

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bioRxiv2021

Assessing The Repeatability of Multi-Frequency Multi-Layer Brain Network Topologies Across Alternative Researchers Choice Paths

There is a growing interest in the neuroscience community on the advantages of multilayer functional brain networks. Researchers usually treated different frequencies separately at distinct functional brain networks. However, there is strong evidence that these networks share complementary information while their interdependencies could reveal novel findings. For this purpose, neuroscientists adopt multilayer networks, which can be described mathematically as an extension of trivial single-layer networks. Multilayer networks have become popular in neuroscience due to their advantage to integrate different sources of information. Here, we will focus on the multi-frequency multilayer functional connectivity analysis on resting-state fMRI recordings. However, constructing a multilayer network depends on selecting multiple pre-processing steps that can affect the final network topology. Here, I analyzed the fMRI dataset from a single human performing scanning over a period of 18 months (84 scans in total), and the second dataset of 25 subjects with 3 repeat scans. I focused on assessing the reproducibility of multi-frequency multilayer topologies exploring the effect of two filtering methods for extracting frequencies from BOLD activity, three connectivity estimators, with or without a topological filtering scheme, and two spatial scales. Finally, I untangled specific combinations of researchers choices that yield consistently brain networks with repeatable topologies, giving us the chance to recommend best practices over consistent topologies.

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bioRxiv2021

Joint modeling of choices and reaction times based on Bayesian contextual behavioral control

In cognitive neuroscience and psychology, reaction times are an important behavioral measure. However, in instrumental learning and goal-directed decision making experiments, findings often rely only on choice probabilities from a value-based model, instead of reaction times. Recent advancements have shown that it is possible to connect value-based decision models with reaction time models. However, typically these models do not provide an integrated account of both value-based choices and reaction times, but simply link two types of models. Here, we propose a novel integrative joint model of both choices and reaction times by combining a mechanistic account of Bayesian sequential decision making with a sampling procedure. This allows us to describe how internal uncertainty in the planning process shapes reaction time distributions. Specifically, we use a recent context-specific Bayesian forward planning model which we extend by a Markov chain Monte Carlo (MCMC) sampler to obtain both choices and reaction times. As we will show this makes the sampler an integral part of the decision making process and enables us to reproduce, using simulations, well-known experimental findings in value based-decision making as well as classical inhibition and switching tasks. Specifically, we use the proposed model to explain both choice behavior and reaction times in instrumental learning and automatized behavior, in the Eriksen flanker task and in task switching. These findings show that the proposed joint behavioral model may describe common underlying processes in these different decision making paradigms. Author summaryMany influential results in psychology and cognitive neuroscience rest on reaction time effects in behavioral experiments, for example in studies about human decision making. For decisions that rest on planning, findings often rely on analyses using specific computational models. Until recently, these models did not allow for analysis of reaction times. In this article we introduce a new model of how to explain both choices and reaction times in decision making experiments that involve planning. Importantly, the model explains how the brain can make good decisions quickly, even in the face of many potential choices and in complex environments.

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